A5000447165- Remote-Sensing Image Classification
- Remote Sensing and Land Use
- Geochemistry and Geologic Mapping
- Metabolomics and Mass Spectrometry Studies
Harbin Engineering University
2023
Ministry of Industry and Information Technology
2023
Change detection based on synthetic aperture radar (SAR) images is an important application in the remote-sensing technology field. However, lack of labeled data has been a difficult problem SAR image detection, especially for pixel-level change detection. In this letter, we propose novel unsupervised algorithm, which improves accuracy by exploring features from both spatial and frequency domains images. particular, first clustering used as preclassification to obtain pseudo-labels then...
Change detection based on synthetic aperture radar (SAR) images is a challenging task in the field of remote sensing image analysis due to influence noise and lack labeled data. In this paper, we propose new unsupervised change algorithm deep learning, which explores spatial frequency domain features SAR parallel improve performance. Our proposed method first obtains pseudo-labels by clustering then combines them with neural networks for detection. To reduce impact sensitivity changes,...